Intelligent deployment cascade control device based on an fdd-ofdma indoor small cell in multi-user and interference environments

ABSTRACT

The invention presents an intelligent deployment cascade control (IDCC) device for frequency division duplexing (FDD)-orthogonal frequency division multiplexing access (OFDMA) indoor small cell to enable easy installation, multi-user (MU) service reliability, optimum throughput, power saving, minimum interference and good cell coverage. The proposed IDCC device is designed with a cascade architecture, which mainly contains five units including a resource allocator, a minimum throughput/cell edge CQI converter, an adaptive neural fuzzy inference system (ANFIS) based initial transmit power setting controller (ITPSC) in the first cascade unit, an ANFIS based channel quality index (CQI) decision controller (CQIDC) in the second cascade unit and an ANFIS based self-optimization power controller (SOPC) in the third cascade unit. The SOPC consists of three parts, namely the transmit power adjustment estimator (TPAE), transmission power assignment and self-optimization power controller protection mechanism.

TECHNICAL FIELD

The invention presents an adaptive neural fuzzy inference system (ANFIS)based intelligent deployment cascade control (IDCC) device for frequencydivision duplexing (FDD)-orthogonal frequency division multiplexingaccess (OFDMA) indoor small cell operated in the multi-user (MU) andinterference environments to self-optimize the MU service reliability(SR), throughput, minimum transmit power and interference for multimediacall services. The proposed IDCC device is designed with a cascadearchitecture, which mainly contains five units including a resourceallocator, a minimum throughput/cell edge CQI converter, an adaptiveneural fuzzy inference system (ANFIS) based initial transmit powersetting controller (ITPSC) in the first cascade unit, an ANFIS basedchannel quality index (CQI) decision controller (CQIDC) in the secondcascade unit and an ANFIS based self-optimization power controller(SOPC) in the third cascade unit. The SOPC consists of three parts,namely the transmit power adjustment estimator (TPAE), transmissionpower assignment and self-optimization power controller protectionmechanism.

BACKGROUND

Currently, the macrocells are deployed by operators. Since thedeployment of femtocells can be in orders of magnitude more numerousthan traditional cellular deployments and a network operator may not beable to control the femtocells directly. The femtocells areself-deployed by users rather than operators. Therefore, the femtocellbase station's (BS) self-optimization deployment control software musthave the characteristics of easy operation to make the BS with the leasthuman action to satisfy the required performance, which are statedhereinafter. The user just needs to plug-and-play and the BS of thefemtocell can automatically configure the system parameters in the MUand interference indoor environments. In addition, the self-optimizationcontrol software deployed the eNode B (eNB) of femtocell in aninterference environment can self-optimization control the transmitpower of the BS to save energy, reduce co-channel interference for theadjacent cell, and meet the requirement of service reliability. Userinput settings include service reliability, the cell edge throughputcorresponding to the cell edge CQI and cell radius to match the size ofthe room coverage. A research report forecasts the global small cellmarket to grow from S690.0 million in 2014 to S4.8 billion by 2019, at aCompound Annual Growth Rate of 41.7%. Thus, the short distance femtocelltechnology in the future development of next-generation wirelesscommunication networks and applications will play a very important role.

A previous study has proposed a coverage adaptation approach forfemtocell deployment in order to minimize the increase of core networkmobility signaling. The information on mobility events of passing andindoor users are used to optimize the femtocell coverage. An approachbased on genetic algorithm was presented in to automatically optimizethe coverage of a group of femtocells in an enterprise environment. Thealgorithm is able to dynamically update the pilot powers of thefemtocells as per the time varying global traffic distribution andinterference levels. The algorithm in a decentralized femtocelldeployment has not been considered. A research report has proposed anadaptive neural fuzzy inference system (ANFIS)-assisted power controlscheme for a multi-rate multimedia direct-sequence code-divisionmultiple-access (DS-CDMA) system to precisely predict the channelvariations and thus compensate for the effect of signal fading inadvance. The author in the above study also provides a procedure fordetermining the transmission rate based upon the output of thesignal-to-interference-plus-noise ratio (SINR) increment of the ANFISpower control mechanisms at the sample period. The fuzzy membershipfunctions of ANFIS power control mechanisms use seven Gaussianfunctions, so that there are 49 fuzzy inference rules. The ANFIS powercontrol mechanisms use two input variables, including SINR error e(n)and SINR error change Δe(n), to track the set point of target SINR. Inthe present technique, the target SINR value is set to a fix value of1.5 dB, let the power control process is not flexible enough. The inputparameters of ANFIS power control mechanism totally depend on SINRcontrol efficiency. The power cannot be controlled by channelenvironment. The technology has not considered the performance ofmulti-user (MU) service reliability (SR).

SUMMARY

In view of the disadvantages of prior art, the primary object of thepresent invention is to present an adaptive neural fuzzy inferencesystem (ANFIS) based intelligent deployment cascade control (IDCC)device for frequency division duplexing (FDD)-orthogonal frequencydivision multiplexing access (OFDMA) indoor small cell operated in themulti-user (MU) and interference environments. The proposed IDCC deviceis designed with a cascade architecture, which mainly contains fiveunits including a resource allocator, a minimum throughput/cell edge CQIconverter, an adaptive neural fuzzy inference system (ANFIS) basedinitial transmit power setting controller (ITPSC) in the first cascadeunit, an ANFIS based channel quality index (CQI) decision controller(CQIDC) in the second cascade unit and an ANFIS based self-optimizationpower controller (SOPC) in the third cascade unit. The SOPC consists ofthree parts, namely the transmit power adjustment estimator (TPAE),transmission power assignment and self-optimization power controllerprotection mechanism. In the experimental example, it assumes that thenumber of indoor users is three, the system bandwidth of the femtocellis set as 20 MHz and the required minimum throughputs for each of usersare 2.76 Mbps, 7.44 Mbps, and 14.13 Mbps, respectively.

The principal object of the present invention is that it canautonomously control the assignments of the resource block, initialpower, the best channel quality index (CQI) and the minimum transmitpower, so that the indoor small cell can produce the optimum throughput,minimum transmit power and interference for multimedia services. Theresource allocator sets the average number of resource blocks for eachindoor user according to the number of users and system bandwidth in thesmall cell. The minimum throughput/cell edge CQI converter sets theminimum (cell edge) CQI for each indoor user in accordance with theminimum (cell edge) throughput requirement. Here the cell edge CQIcorresponds to the SINR threshold for the BLER of the transceiver equalto 10-1. The present invention uses cascade ANFIS architecture to adaptthe initial transmit power setting to the requested throughput at thecell edge, coverage radius and the allocated number of resource blocks;to adapt the best CQI to the initial transmit power setting and averagepath loss (PL) measured by user equipment (UE) and the allocated numberof resource blocks; to adapt the transmit power adjustment estimator(TPAE) in SOPC unit to the requested CQI at the cell edge, the best CQIand measured average SINR. The present IDCC device is design toself-optimize the signal-to-interference-plus-noise (SINR) andthroughput service reliabilities of the indoor small cell in themulti-user (MU) and interference environments, while maintaining theblocking error rate (BLER) less than 10-1 and minimizing the transmitpower and interference power to achieve the aims of energy saving andinterference reducing.

Other aspects and advantages of the present invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given herein below and the accompanying drawingswhich are given by way of illustration only, and thus are not limitativeof the present disclosure and wherein:

FIG. 1 is a block diagram of intelligent deployment cascade control(IDCC) device for FDD-OFDMA indoor small cell.

FIG. 2 shows the architecture of an ANFIS based initial transmit powersetting controller (ITPSC) unit.

FIG. 3 shows the architecture of an ANFIS based channel quality index(CQI) decision controller (CQIDC) unit.

FIG. 4 shows the architecture of an ANFIS based transmit poweradjustment estimator in SOPC unit.

FIG. 5 illustrates the BLER performance of DL OFDM transceiver in IOAchannel for CQI=1, 2 . . . , 15.

FIG. 6 illustrates average throughput and system capacity of DL OFDMtransceiver in IOA channel for CQI=1, 2 . . . , 15.

FIG. 7 shows the scenario of laboratory with femtocell eNB andmeasurement points.

FIG. 8 shows the modified path loss model obtained from the field trialin the laboratory.

FIG. 9 shows a set of training data for ITPSC unit: The minimum transmitpower corresponding to coverage radius=5 m, the cell edge CQI=3, 7, 10and number of resource block=1 to 100.

FIG. 10(a) shows the initial coverage radius (R) membership functions ofITPSC unit.

FIG. 10(b) shows the initial number of resource block (nRB) membershipfunctions of ITPSC unit.

FIG. 10(c) shows the initial cell edge CQI (CQI_(min)) membershipfunctions of ITPSC unit.

FIG. 11(a) shows the learned coverage radius (R) membership functions ofITPSC unit.

FIG. 11(b) shows the learned number of resource block (nRB) membershipfunctions of ITPSC unit.

FIG. 11(c) shows the learned cell edge CQI (CQI_(min)) membershipfunctions of ITPSC unit.

FIG. 12 shows the root mean square error of ITPSC unit.

FIG. 13 shows a set of training data for CQIDC unit: The best CQIcorresponding to the initial transmit power of ITPSC=−40 dBm, −20 dBm, 0dBm, average path loss=30 dB to 70 dB and number of resource block=1 to100.

FIG. 14(a) shows the initial average path loss (PL) membership functionsof CQIDC unit.

FIG. 14(b) shows the initial transmit power initialization (P_(ini))membership functions of CQIDC unit.

FIG. 14(c) shows the initial number of resource block (nRB) membershipfunctions of CQIDC unit.

FIG. 15(a) shows the learned average path loss (PL) membership functionsof CQIDC unit.

FIG. 15(b) shows the learned transmit power initialization (P_(ini))membership functions of CQIDC unit.

FIG. 15(c) shows the learned number of resource block (nRB) membershipfunctions of CQIDC unit.

FIG. 16 shows the root mean square error of CQIDC unit.

FIG. 17 shows a set of training data for the ANFIS based TPAE in theSOPC unit: The optimum power adjustment corresponding to CQI_(min)=6,the best CQI=7, 10, and different average measured SINR.

FIG. 18(a) shows the initial cell edge CQI (CQI_(min)) membershipfunctions of the ANFIS based TPAE in the SOPC unit.

FIG. 18(b) shows the initial best CQI (CQI_(best)) membership functionsof the ANFIS based TPAE in the SOPC unit.

FIG. 18(c) shows the initial measured average SINR (SINR) membershipfunctions of the ANFIS based TPAE in the SOPC unit.

FIG. 19(a) shows the learned cell edge CQI (CQI_(min)) membershipfunctions of the ANFIS based TPAE in the SOPC unit.

FIG. 19(b) shows the learned best CQI (CQI_(best)) membership functionsof power increment estimator unit.

FIG. 19(c) shows the learned measured average SINR (SINR) membershipfunctions of the ANFIS based TPAE in the SOPC unit.

FIG. 20 illustrates the root mean square error of the ANFIS based TPAEin the SOPC unit.

FIG. 21 shows the flow chart of power allocation algorithm.

FIG. 22 shows the simulation flow chart of IDCC.

FIG. 23(a) shows the CCDF of SINR measurement for different UEs usingfixed power and the IDCC in interference free environment.

FIG. 23(b) shows the CCDF of SINR measurement for different UEs usingfixed power and the IDCC in interference power=−90 dbm environment.

FIG. 23(c) shows the CCDF of SINR measurement for different UEs usingfixed power and the IDCC in interference power=−80 dbm environment.

FIG. 24(a) shows the CCDF of throughput for different UEs using fixedpower and the IDCC in interference free environment.

FIG. 24(b) shows the CCDF of throughput for different UEs using fixedpower and the IDCC in interference power=−90 dbm environment.

FIG. 24(c) shows the CCDF of throughput for different UEs using fixedpower and the IDCC in interference power=−80 dbm environment.

FIG. 25 shows the average throughput of DL transceiver for different UEsusing fixed power and the IDCC in the MU interference environment.

FIG. 26 shows the average transmit power of DL OFDM transceiver fordifferent UEs using the IDCC in the MU interference environment.

DETAILED DESCRIPTION

For your esteemed members of reviewing committee to further understandand recognize the fulfilled functions and structural characteristics ofthe invention, several preferable embodiments cooperating with detaileddescription are presented as the follows.

The invention presents an adaptive neural fuzzy inference system (ANFIS)based intelligent deployment cascade control (IDCC) device for FDD-OFDMAindoor small cell operated in the multi-user (MU) and interferenceenvironments to self-optimize the MU service reliability (SR), averagethroughput, transmit power and interference for multimedia callservices.

The principal structure of the present invention is an ANFIS based IDCCdevice as shown in FIG. 1; which mainly contains five units including aresource allocator, a minimum throughput/cell edge CQI converter, anadaptive neural fuzzy inference system (ANFIS) based initial transmitpower setting controller (ITPSC) in the first cascade unit, an ANFISbased channel quality index (CQI) decision controller (CQIDC) in thesecond cascade unit and an ANFIS based self-optimization powercontroller (SOPC) in the third cascade unit. The SOPC consists of threeparts, namely the transmit power adjustment estimator (TPAE),transmission power assignment and self-optimization power controllerprotection mechanism.

In order to complete the intelligent deployment of small cells, thepresent invention is to use adaptive network architecture established byJjh Shing Roger Jang in 1993, known as ANFIS, which is a fuzzy inferencesystem. By using a hybrid learning method, the weights of ANFIScontroller are adjusted to the appropriate value. The user inputs theparameters including the service reliability, coverage radius and thethroughput at the cell edge. The user equipment (UE) measures thereference signal received power (RSRP) and sends back the estimatedaverage path loss (PL) and signal-to-interference-plus-noise ratio(SINR) to the IDCC device. The proposed IDCC device is design toself-optimize the minimum transmit power of the indoor small cell in themulti-user (MU) and interference environments, while maintaining theblocking error rate (BLER) of the transceiver less than 10-1, andsatisfying the requirements of average throughput and servicereliability for the UE.

The architecture diagram of ANFIS based ITPSC unit is shown in FIG. 2,which contains five layers, a total of three inputs and one output.Three input parameters for the u_(th) user are the coverage radius ofindoor office (R_(u)), the number of resource blocks (nRB_(u)) and thecell edge CQI that is defined as CQI_(min,u), the output parameter forthe u_(th) user is an initial minimum transmit power (P_(ini,u)). TheITPSC unit adapts the initial power setting to the changing R_(u),nRB_(u) and CQI_(min,u). The generalized bell shape membership functionof each input parameter for the u_(th) user is divided into threelevels. There are 27 fuzzy inference rules. The ITPSC unit defines theR_(u) of less than 5 meters as low (L), it defines 5 meters˜10 meters asmedium (M), it defines 11 meters 15 meters as high (H); it defines thenRB_(u) of 1 RB˜25 RBs as low (L), it defines 26 RBs˜74 RBs as medium(M), it defines 75 RBs˜100 RBs as high (H); it defines CQI_(min,u) in1˜5 for L, it defines CQI_(min,u) in 6˜10 for M, it defines CQI_(min,u)in 11˜15 for H; the output for the u_(th) user is the initial transmitpower setting (P_(ini,u)), which satisfies the u_(th) user requestsunder interference free environments.

The architecture diagram of ANFIS based CQIDC unit is shown in FIG. 3,which contains five layers, a total of three inputs and one output.There are three input parameters for the u_(th) user including averagepath loss (PL _(u)) between orthogonal frequency division multiplexing(OFDM) transmitter and receiver, initial transmit power setting(P_(ini,u)) and number of resource blocks (nRB_(u)), the outputparameter for the u_(th) user is the best CQI (CQI_(best,u)). Underinterference free environments, the CQIDC unit adapts the best CQI tothe changing PL _(u), P_(ini,u) and nRB_(u). The Gaussian shapemembership function of each input parameter is divided into threelevels. There are 27 fuzzy inference rules. The CQIDC unit defines theaverage path loss (PL _(u)) in 30 dB˜40 dB for L, it defines averagepath loss (PL _(u)) in 41 dB˜60 dB for M, it defines average path loss(PL _(u)) in 61 dB˜70 dB for H; it defines initial transmit powersetting (P_(ini,u)) in −75 dBm˜51 dBm for L, it defines initial transmitpower setting (P_(ini,u)) in −50 dBm˜4 dBm for M, it defines the initialtransmit power setting (P_(ini,u)) in −3 dBm˜20 dBm for H; it definesthe nRB_(u) of 1 RB˜25 RBs as low (L), it defines 26 RBs 74 RBs asmedium (M), it defines 75 RBs˜100 RBs as high (H). The output for theu_(th) user is the best CQI (CQI_(best,u)) under interference freeenvironments.

The SOPC consists of three parts, namely the transmit power adjustmentestimator (TPAE), transmission power assignment and self-optimizationpower controller protection mechanism. The power adjustment estimator inthe interference environment primarily estimates the amount of minimumtransmit power adjustment needs for each user; the transmission powerfor each user is adjusted when the sum doesn't exceed the maximumtransmit power limit. The protection mechanism of the SOPC is used toprevent the co-channel interference from the moving users of adjacentcells.

The architecture diagram of ANFIS based TPAE in the SOPC unit is shownin FIG. 4, which contains five tiers, a total of three inputs for theu_(th) user including cell edge CQI (CQI_(min,u)), best CQI(CQI_(best,u)) and average measured SINR (SINR _(u)), the outputparameter for the u_(th) user is a power adjustment (ΔP_(u)). In theinterference environment, the ANFIS based TPAE in the SOPC unit adaptsoutput power adjustment ΔP_(u) to the changing CQI_(best,u) and SINR_(u). The SOPC unit will be coordinated with CQIDC unit to set theminimum transmit power for the transceiver, which switches to thecorresponding modulation mode and coding rate. The SOPC unit acceptsthree inputs and generates the optimizing minimum transmit power. TheSOPC unit will continue to estimate the average SINR value in the MUinterference environments. The transmit power of OFDM transceiver willbe adjusted with the changing interference power to maintain the BLERless than 10⁻¹ and to satisfy the requirement of SR for the u_(th) user;The generalized bell shape membership function of each input parameteris divided into three levels. There are 27 fuzzy inference rules. TheANFIS based TPAE in the SOPC unit defines the CQI_(min,u) in 1˜5 for L,it defines CQI_(min,u) in 6˜10 for M, it defines CQI_(min,u) in 11˜15for H; it defines the CQI_(best,u) in 1˜5 for L, it defines CQI_(best,u)in 6˜10 for M, it defines the CQI_(best,u) in 11˜15 for H; it definesthe SINR _(u) less than −25 dB˜5 dB for L, it defines the SINR _(u) in−4 dB˜25 dB for M, it defines the SINR _(u) in 26 dB˜45 dB for H. Thesignal-to-interference-plus-noise ratio (SINR) is estimated through thereference signal received power (RSRP) measured from the user equipment(UE) and sends back to the IDCC device in e Node B (eNB). The estimatedpath loss (PL) is obtained by subtracting RSRP from the transmitreference signal.

(A) The Architecture of the ANFIS Controller:

The ANFIS based TPAE in the SOPC unit is used as an example to describethe framework of the ANFIS controller:

Layer 1: The generalized bell shape membership functions are defined as:

$\begin{matrix}{{{A_{j,n}\left( x_{j,m} \right)} = \frac{1}{1 + {\frac{x_{j,m} - c_{j,n}}{a_{j,n}}}^{2b_{j,n}}}},{{{for}\mspace{14mu} n} = 1},2,{{3\mspace{14mu} {and}\mspace{14mu} j} = 1},2,3} & (1)\end{matrix}$

where x_(j,m) is the m_(th) input and the premise parameters a_(j,n),b_(j,n), c_(j,n) pertaining to the node outputs are updated according togiven training data and the gradient descent approach.

Layer 2: The output of node i, denoted by O_(2,i), is the product of allthe incoming signals for the i_(th) rule. It is given by:

w _(i,m) =O _(2,i) =A _(1,p)(x _(1,m))×A _(2,q)(x _(2,m))×A _(3,r)(x_(3,m))

for i=1,2,27;p=1,2,3;q=1,2,3;r=1,2,3.  (2)

Layer 3: The output of node i, denoted by O₃, is called the normalizedfiring strength and calculated as:

$\begin{matrix}{{O_{3,i} = {{\hat{w}}_{i,m} = \frac{w_{i,m}}{\sum\limits_{i = 1}^{27}w_{i,m}}}},{{{for}\mspace{14mu} i} = {1\text{∼}27}}} & (3)\end{matrix}$

Layer 4: Every node in the fourth layer is an adaptive node with a nodefunction:

O _(4,i) =ŵ _(i,m) ×f _(i,m) =ŵ _(i,m)×(α_(i) x _(1,m)+β_(i) x_(2,m)+γ_(i) x _(3,m)+ω_(i));

for i=1˜27  (4)

where O_(4,i) is the node output, f_(i,m) is a crisp output in theconsequence, and the α_(i), β_(i), γ_(i), γ_(i), ω_(i) are theconsequent parameters of node i. The 27 fuzzy inference rules of f_(i,m)are constructed as follows:

R ₁: if (x _(1,m) is A ₁₁) and (x _(2,m) is A ₂₁) and (x _(3,m) is A ₃₁)then (output is f _(1,m));

R ₂: if (x _(1,m) is A ₁₁) and (x _(2,m) is A ₂₁) and (x _(3,m) is A ₃₂)then (output is f _(2,m));

R ₃: if (x _(1,m) is A ₁₁) and (x _(2,m) is A ₂₁) and (x _(3,m) is A ₃₃)then (output is f _(3,m));

R ₂₆: if (x _(1,m) is A ₁₃) and (x _(2,m) is A ₂₃) and (x _(3,m) is A₃₂) then (output is f _(26,m));

.

.

.

R ₂₇: if (x _(1,m) is A ₁₃) and (x _(2,m) is A ₂₃) and (x _(3,m) is A₃₃) then (output is f _(27,m))  (5)

The above 27 fuzzy inference rules are used for determining the assigneddata rate to achieve optimization objective.

Layer 5: The single node in the fifth layer is a fixed node labeled Σ,which computes the overall output O₅ as the summation of all incomingsignals.

$\begin{matrix}{G_{m} = {O_{5} = {\sum\limits_{i = 1}^{27}{{\hat{w}}_{i,m} \times f_{i,m}}}}} & (6)\end{matrix}$

(B) The Minimum Throughput/Cell Edge CQI Conversion Unit:

In order to satisfy the user requirements of indoor small cell inthroughput and blocking error rate (BLER) of less than 10⁻¹, therelationship between the throughput and SINR threshold for the differentCQI must be obtained. Therefore, the BLER and throughput of the LTEdownlink (DL) transceiver for indoor small cell are simulated togenerate the training data for the ANFIS ITPSC. The system parametersare shown in Table 1 and fundamental parameters of the transceiver areshown in Table 2. In the simulation of the present embodiment, for thedifferent channel quality index (CQI), the BLER of 1×1 SISO-OFDMtransceiver is simulated where the system bandwidth is 20 MHz, theindoor office A (IOA) channel is selected as channel model, the leastsquare (LS) channel estimation and minimum mean square error (MMSE)equalizer are used, and the user speed is assumed to be 10 km/hr. The1000 sub frames are applied for the simulations. The results are shownin FIG. 5, which is used as a training data to define the SINR thresholdfor BLER=10⁻¹ under different CQI, as shown in Table 3. In each CQI(corresponding to each pair of modulation and code rate mode), thedifference between the SINR measurement value and the SINR thresholdvalue is used to control the size of the transmit power adjustment ΔP,which must compliance with the provisions of Table 3, where theinterference power is not considered. The physical resource blocks(PRBs) of Table 3 is generated according to the system bandwidth of 20MHz and three indoor users. When the system bandwidth is 20 MHz, thecorresponding total number of physical resource blocks (PRBs) is 100PRBs.

The resource assignment method of this invention is the orthogonalfrequency division multiplexing access (OFDMA) for the frequencydivision duplexing (FDD) mode of indoor small cell operated in themulti-user (MU) environments. The eNB of the indoor office will performthe resource allocation for each UE with 33 RBs at each time instant.For practical implementation considerations, the system capacity of thedownlink (DL) OFDM transceiver formula is modified as [10]:

$\begin{matrix}{{C({bps})} = {{BW} \cdot {BW\_ eff} \cdot \frac{{nRB}_{u}}{{nRB}_{total}} \cdot \eta \cdot {\log_{2}\left( {1 + {{SINR}/{SINR\_ eff}}} \right)}}} & (7)\end{matrix}$

where nRB_(total) is the total number of RBs and nRB_(u) is the numberof RBs assigned for the u_(th) user; BW and BW_eff are system bandwidthand effective system bandwidth, respectively. The parameter η is acorrection factor. SINR and SINR_eff are signal to interference plusnoise power ratio and effective signal to interference plus noise powerratio, respectively. In this invention, the simulation parameters of DLSISO OFDM transceiver is given in Table 1, where BW=20 MHz, BW_eff=0.83,η=0.43 and SINR_eff=2.51199 (4 dB). The average throughput of DLtransceiver in IOA channel for CQI=1, 2 . . . , 15 is shown in FIG. 6,where the system capacity is denoted by dotted curve and the simulatedaverage throughput for CQI=1, 2 . . . , 15 are denoted by solid curve.It is observed that the simulated average throughput approximates to theShannon capacity bound for LTE specifications. Thus, the throughputscorresponding to SINR threshold for the different CQIs are calculatedwith (7) and listed in Table 3. As can be seen from table, the inputthroughput requirement settings for the u_(th) user in the range of1.99˜2.76 Mbps, 6.76˜7.44 Mbps, 12.99˜14.13 Mbps correspond to the celledge CQI (CQI_(min,u)) in 3, 7, and 10, respectively.

(C) Initial Transmit Power Setting Controller (ITPSC) Unit:

In order to control the initial transmit power of small cell eNB forsatisfying the requirements of the u_(th) user, the BLER performance ofthe LTE downlink (DL) transceiver is simulated to generate the trainingdata for the ITPSC. This invention considers multi-user systemreliability (SR) requirements of indoor small cell in fadingenvironments. The received signal strength P_(r) at the UE islog-normally distributed. The coverage probability of P_(r) greater thanthe receiver sensitivity P_(r,min) from the femtocell to a UE at thedistance d is:

$\begin{matrix}\begin{matrix}{{P_{W_{0}}(d)} = {{p\left\lbrack {P_{r} \geq P_{r,\min}} \right\rbrack} = {\int_{W_{0}}^{\infty}{{p\left( P_{r} \right)}{dP}_{r}}}}} \\{= {\frac{1}{2} - {\frac{1}{2}{{erf}\left( \frac{P_{r,\min} - K + {10\; N\; {\log_{10}\left( \frac{d}{R} \right)}}}{\sqrt{2}\sigma_{W}} \right)}}}}\end{matrix} & (8)\end{matrix}$

where R is the coverage radius, K is the average signal strength (dBm)at the cell edge, K−P_(r,min) (dB) is the fade margin (FM) at the celledge (d=R) which is used to guarantee the reliability at the cell edge,σ_(W) is the standard deviation of received signal strength (dB) and Nis the path loss exponent.

The percentage of the UE in a cell of radius R for P_(r) greater thanthe receiver sensitivity P_(r,min) is defined as the service reliability(SR), which is given as:

$\begin{matrix}{{{S\; R} = {\frac{1}{2}{\left\{ {1 + {{erf}(p)} + {{\exp \left( \frac{{2\; {pq}} + 1}{q^{2}} \right)}\left\lbrack {1 - {{erf}\left( \frac{{pq} + 1}{q} \right)}} \right\rbrack}} \right\}.}}},} & (9) \\{where} & \; \\{{p = \frac{F\; M}{\sqrt{2}\sigma_{W}}},{q = \frac{10\; N\; \log_{10}e}{\sqrt{2}\sigma_{W}}},} & (10)\end{matrix}$

The minimum transmit power of the ITPSC is evaluated by link budgetformula for the different SR, coverage radius (R_(u)) of indoor office,and the cell edge CQI (CQI_(min,u)) requested by the u_(th) user. Theminimum transmit power in dBm of the ITPSC is given by:

P _(ini,u) =P _(rmin,u)(CQI_(min,u))+L _(t) −G _(t)+PL(R_(u))+FM(SR_(u))−G _(r) +L _(r)  (11)

where P_(rmin,u)(CQI_(min,u)) is the receiver sensitivity of the celledge CQI (CQI_(min,u)) for the u_(th) user. L_(t) denotes the cable lossin dB. G_(t) and G_(r) are the antenna gains in dBi of the femtocell andthe UE, respectively. PL(R_(u)) denotes the maximum path loss between afemtocell and the uth user at the cell edge. L_(r) in dB is the bodyloss of the UE. FM(SR_(u)) denotes fade margin in dB corresponding tothe SR set by the u_(th) user. The receiver sensitivity of the givencell edge CQI (CQI_(min,u)) for the u_(th) user is obtained by:

P _(rmin,u)(CQI_(min,u))=P _(N,u)+SNR_(th)(CQI_(min,u))  (12)

where SNR_(th)(CQI_(min,u)) denotes the SNR threshold of the receiverfor different CQI_(min,u), which is generated from the performancesimulations using the transceiver specification listed in Table 2. Thereceiver noise power P_(N,u) in dBm for the u_(th) user is given as:

P _(N,u)=NF(dB)+(−174)+10 log₁₀(BW_(r,u))(dBm)  (13)

where NF is the noise FIG. of the UE receiver and BW_(r,u) is thereceiver bandwidth.

BW_(r,u)=15 kHz×12×nRB_(u)  (14)

where nRB_(u) is the allocated RBs of the u_(th) user. The SNRthresholds for BLER=0.1 are summarized in Table 3. Using the ITU-Rindoor path loss model [12], the path loss between a femtocell eNB andan UE separated by a distance d (m) in a given cell is

PL(d)=20 log₁₀(f)+10N log₁₀(d)+L _(f)(n)−28 (dB)  (15)

where the carrier frequency f (MHz) is set as 2350 MHz with 20 MHzbandwidth in the experiment. N is the path loss exponent, where thenominal value in the indoor office is set as 3 [12]. Lin) (dB) is thepenetration loss between the floors, where n is the number of floors.The penetration loss is not considered in the simulations.

In addition, the standard deviation σ_(W) of the received shadow fadingsignal power in the indoor office environment is set as 10 dB.

Experiment Measurements in the Laboratory:

For the purpose of determining the path loss exponent N and the standarddeviation σ_(W) of the received shadow fading signal in the indooroffice environments, the power measurement of small cell eNB(ITRI-SC-CUT3) is performed in the laboratory. The scenario oflaboratory is shown in FIG. 7, where the femtocell eNB and measurementpositions are illustrated. The length and width of the laboratory are20.98 meter and 7.30 meter, respectively. The black stars located in thelaboratory represent 38 measurement positions, which are distributed atdistance of 1˜19 meters from the eNB. 1000 RSRPs are measured at eachmeasurement position.

UE (Samsung Galaxy Note Edge SM-N915G) used with drive test tool reportRSRPs for different distances between transmitter and receiver in thelaboratory and calculate their standard deviation. Then the path lossmodel of the laboratory can be obtained by modifying the ITU-R indooroffice path loss model. FIG. 8 shows the modified path loss modelobtained from the field trial in the laboratory, where circle solidcurve is the ITU-R indoor office path loss model; the triangular dottedcurve is the measured path loss model; the square solid curve is themodified path loss model of the laboratory. Therefore, the formula ofthe ITU path loss model is modified as

PL(d)=20 log₁₀(f)+28 log₁₀(d)−36 (dB)  (16)

where the standard deviation σ_(W) of the received shadow fading signaland the path loss exponent N are 4.27 dB and 2.8, respectively. Finally,by substituting O and N into (9)(10), the fade margin FM for 90% servicereliability is calculated as 2.14 dB.

The training data of the ITPSC is generated from the simulation resultsof the transceiver BLER, as shown in Table 3. Integrating Table 3 withequations (11), (12), (13), (14) and (16), the minimum transmit power iscalculated for the service reliability of 90%, different coverage radius(2.5, 5, 7.5, 10, 12.5 and 15 meters), different number of resourceblock (1˜100) and cell edge CQI (1˜15). FIG. 9 shows a set of trainingdata of the ITPSC for coverage radius of 5 meter, cell edge CQI (3, 7,10) and different number of resource block (1˜100). For example, whenthe number of resource block is 50, the minimum transmit powercorresponding to different cell edge CQI (3, 7, 10) is (−35.33 dBm,−27.33 dBm, −18.33 dBm).

The function of the ITPSC is to set the initial minimum transmit powerof the femtocell eNB, which satisfies the requirements of the differentRBs (nRB_(u)), coverage radius (R_(u)) of indoor office, and the celledge CQI (CQI_(min,u)) requested by the u_(th) user in the interferencefree environments. Each input uses three generalized bell shapemembership functions (MFs), which are defined as:

$\begin{matrix}{{{A_{j,n}\left( x_{j,m} \right)} = \frac{1}{1 + {\frac{x_{j,m} - c_{j,n}}{a_{j,n}}}^{2b_{j,n}}}},{{{for}\mspace{14mu} n} = 1},2,{{3\mspace{14mu} {and}\mspace{14mu} j} = 1},2,3} & (17)\end{matrix}$

where x_(j,m) is the m_(th) input and the premise parameters a_(j,n),b_(j,n), c_(j,n) pertaining to the node outputs are updated according togiven training data and the steepest descent approach. The 27 fuzzyinference rules are constructed in Table 4. A minimum transmit poweroptimization problem of the ANFIS-ITPSC for the u_(th) user is formallyformulated as follows:

$\begin{matrix}{{{{{Optimize}\mspace{14mu} P_{ini}} = {f\left( \overset{\rightarrow}{x} \right)}},{{{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {objective}\mspace{14mu} {function}}\;;}}{{subject}\mspace{14mu} {to}\text{:}}{\overset{\rightarrow}{x} \in \left\{ {R_{u},{nRB}_{u},{CQI}_{\min,u}} \right\}}{{0\mspace{14mu} m} < R_{u} \leq {15\mspace{20mu} m}}{1 \leq {nRB}_{u} \leq 100}{1 \leq {CQI}_{\min,u} \leq 15}{{\sum\limits_{u}^{nUE}P_{{ini},u}} \in \left\{ {\leq {20\mspace{14mu} {dBm}}} \right\}}} & (18)\end{matrix}$

The premise parameters of three MFs before and after training are shownin FIG. 10 and FIG. 11, respectively. The root mean square error (RMSE)curve of the ITPSC is shown in FIG. 12, which demonstrates that the RMSEconverges to 0.9 dBm after 300 epochs.

(D) Channel Quality Index Decision Controller (CQIDC) Unit:

In the real radio channel environment, indoor small cell base stationdeployment will face co-channel interference of macro cell base stationor neighboring small cell, resulting in performance degradation ofindoor small cell base station. Therefore, the CQIDC unit in the IDCCdevice determines the best CQI in interference-free environment to meetthe receiver performance of BLER≤0.1. Further, in interferenceenvironments, self-optimizing power system control unit (SOPC) keepstrack of the measured SINR to self-optimize the transmit power, enablingthe UE to meet the objective needs of the service's reliability andminimum transmit power.

In the interference free environment, in order to determine the best CQI(CQI_(best,u)) at the u_(th) user's location of the indoor office, thefollowing formula is used to estimate signal-to-noise-power ratio (SNR).It can be expressed as

SNR_(u) =P _(r,u)(W)/P _(N,u)(W)  (19)

where the average received power P_(r,u) at the u_(th) user in theinterference free environment is given as

P _(r,u) =P _(ini,u) −L _(t) +G _(t)−PL _(u) +G _(r) −L _(r)  (20)

where PL _(u) denotes the measured average path loss between a femtocelland an UE in the given cell. The noise power P_(N,u) can be calculatedby (13). Then, the best CQI in the interference free environment isdetermined by the following rules:

$\begin{matrix}{{CQI}_{{best},u} = \left\{ \begin{matrix}{{CQI\_ i},} & {{{{if}\mspace{14mu} {{SNR}_{th}({CQI\_ i})}} \leq {SNR}_{u} < {{SNR}_{th}\left( {{CQI\_ i} + 1} \right)}},{i = {1\text{∼}14}}} \\{{{CQI\_}15},} & {{{if}\mspace{14mu} {SNR}_{u}} \geq {{SNR}_{th}\left( {{CQI\_}15} \right)}} \\{{{CQI\_}1},} & {{{if}\mspace{14mu} {SNR}_{u}} < {{SNR}_{th}\left( {{CQI\_}1} \right)}}\end{matrix} \right.} & (21)\end{matrix}$

The training data of the CQIDC is generated from the simulation resultsof the transceiver BLER, as shown in Table 3. Integrating Table 3 withequations (19), (20) and (21), the best CQI is calculated for differentmeasured average path loss (30 dB˜70 dB), resource block (1˜100) andinitial minimum transmit power (−75 dBm˜20 dBm). FIG. 13 shows a set ofCQIDC training data of the best CQI corresponding to different averagepath loss (30 dB˜70 dB), when the resource block is 50, the minimumtransmit power is (−40 dBm, −20 dBm, 0 dBm). It is observed that thebest CQI will decrease as the path loss becomes larger, and at the sameaverage path loss, the best CQI increases as the initial transmit powerbecomes larger.

The function of the CQIDC is to determine the best CQI of the femtocellat the u_(th) user's location of indoor office, which satisfies thereceiver performance of BLER≤0.1 in the interference free environments.Each input uses three Gaussian MFs, which are defined as

$\begin{matrix}{{{A_{j,n}\left( x_{j,m} \right)} = e^{\frac{- {({x_{j,m} - b_{j,n}})}^{2}}{2a_{j,n}^{2}}}},{{{for}\mspace{14mu} n} = 1},2,{{3\mspace{14mu} {and}\mspace{14mu} j} = 1},2,3} & (22)\end{matrix}$

where x_(j,m) is the ni_(th) input and the premise parameters a_(j,n),b_(j,n) pertaining to the node outputs are updated according to giventraining data and the steepest descent approach. The 27 fuzzy inferencerules are constructed in Table 5. The output of CQIDC is the best CQI ofthe femtocell at the u_(th) user's location of indoor office. Anoptimization problem of the best CQI of the ANFIS-CQIDC is formallyformulated as follows:

In the interference free environments, BLER ≤ 0.1, optimize CQI_(best,u)= f ({right arrow over (x)})at the u_(th)user, f ({right arrow over(x)}) is the objective function; subject to : {right arrow over (x)} ∈{PL _(u), P_(ini,u),nRB_(u)} 30dB ≤ PL _(u) ≤ 70dB −75dBm ≤ P_(ini,u) ≤20dBm 1 ≤ nRB_(u) ≤ 100 CQI_(best,u) ∈ {1 ~ 15} (23)

The premise parameters of three MFs before and after training are shownin FIG. 14 and FIG. 15, respectively. The root mean square error (RMSE)curve of the CQIDC is shown in FIG. 16, which demonstrates that the RMSEconverges to 0.46 after 300 epochs.

(E) Self-Optimizing Power Control (SOPC) Unit:

The SOPC consists of three parts, namely the transmit power adjustmentestimator (TPAE), transmission power assignment and self-optimizationpower controller protection mechanism. The TPAE in the interferenceenvironment primarily estimates the amount of minimum transmit poweradjustment needs for each user; the transmission power for each user isadjusted when the sum of total transmission power to all indoor usersdoesn't exceed the maximum transmit power limit of the eNB. Theprotection mechanism of the SOPC is used to prevent the co-channelinterference from the moving users of adjacent cells.

The ANFIS based TPAE of the SOPC unit adapts output power adjustmentvalue ΔP_(u) at the u_(th) user's location to the changing cell edge CQI(CQI_(min,u)) set by user demand, the best CQI (CQI_(best,u)) andmeasured average SINR (SINR _(u)), so that the femtocell in theinterference environment not only can still meet the demands set by theUE and communication quality, but also self-optimizing the minimumtransmit power of eNB, thereby reducing the co-channel interference tothe neighboring cells.

For the purpose of satisfying the requirements of BLER≤10% and the SR of90%, the threshold of the signal to interference plus noise ratio(SINR_(th,u)) at the) u_(th) user is defined as

SINR_(th,u)=max{SNR_(th)(CQI_(min,u))+FM(SR_(u)),SNR_(th)(CQI_(best,u))}(dB)  (24)

The output power adjustment (ΔP_(u)) at the u_(th) user is given as

ΔP _(u)=SINR_(th,u)−SINR _(u)(dB)  (25)

where SINR _(u) is the measured average SINR at the u_(th) user.

The training data of ANFIS based TPAE of the SOPC unit is generated fromthe simulation results of the single input single output (SISO)transceiver BLER, as shown in Table 3. The fundamental specification ofthe SISO transceiver is listed in Table 2. Integrating Table 2, Table 3with equations (24) and (25), the adjustment value of the minimumtransmit power is calculated for service reliability (90%), cell edgeCQI (1˜15), measured average SINR (−25 dB˜45 dB) and the best CQI(1˜15). FIG. 17 shows a set of SOPC training data for the transmit poweradjustment estimator corresponding to the different measured averageSINR when the service reliability is 90%, cell edge CQI (CQI_(min,u)) is6 for two different best CQI (CQI_(best,u))(7, 10). It is observed thatthe transmit power adjustment will decreases as the measured averageSINR increases in order to achieve the aim of minimum transmit power.For example, the SNR threshold for CQI_(best,u)=7 is 13 dB. According to(20), the SINR threshold (SINR_(th,u)) is equal to 14.14 dB, which isthe sum of SNR threshold (12 dB) corresponding to CQI_(min,u)=6 andFM(SR_(u))(2.14 dB) corresponding to SR_(u)=90%. The SNR threshold forCQI_(bes,u)=10 is 22 dB, which is greater than 14.14 dB. Thus, the SINRthreshold (SINR_(th,u)) is equal to 22 dB for CQI_(best,u)=10. FIG. 13shows ΔP_(u)=0 dB when the measured average SINR is 14.14 dB and 22 dBcorresponding to CQI_(best,u)=7 and 10, respectively.

The function of ANFIS based TPAE of the SOPC unit is to determine theminimum transmit power of the femtocell eNB, which satisfies thereceiver performance of BLER≤0.1 in the interference environments. Eachinput uses three generalized bell shape MFs, which are defined in (1).The 27 fuzzy inference rules are constructed in Table 6. Optimizationproblem of the minimum transmit power of the ANFIS based TPAE in theSOPC unit is formally formulated as follows:

$\begin{matrix}{{{{In}\mspace{14mu} {the}\mspace{14mu} {interference}\mspace{14mu} {environment}},{{BLER} \leq 0.1},{{{optimize}\mspace{14mu} \Delta \; P_{u}} = {{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {at}\mspace{14mu} {the}\mspace{14mu} u_{th}\mspace{14mu} {user}}},{{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {objective}\mspace{14mu} {function}\text{:}}}{\overset{\rightarrow}{x} \in \left\{ {{CQI}_{\min,u},{{CQI}_{{best},u}\mspace{14mu} {and}\mspace{14mu} {\overset{\_}{SINR}}_{u}}} \right\}}{1 \leq {CQI}_{\min,u} \leq 15}{1 \leq {CQI}_{{best},u} \leq {15 - {25\mspace{14mu} {dB}}} \leq {\overset{\_}{SINR}}_{u} \leq {45\mspace{14mu} {dB}}}{{{{\sum\limits_{u}^{nUE}P_{u}} + {\Delta \; P_{u}}} \in \left\{ {\leq {20\mspace{14mu} {dBm}}} \right\}},{P_{u}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {last}\mspace{14mu} {transmit}\mspace{14mu} {power}\mspace{14mu} ({dBm})}}} & (26)\end{matrix}$

The premise parameters of three MFs before and after training are shownin FIG. 18 and FIG. 19, respectively. The root mean square error (RMSE)curve of the TPAE is shown in FIG. 20, which demonstrates that the RMSEconverges to 0.86 dB after 300 epochs.

Power Assignment Algorithm

For the purpose of controlling the sum of individual transmission powerto all indoor users less than the maximum transmit power limit of theindoor small cell eNB, a power assignment algorithm is proposed in FIG.21. First, the previous transmit power P_(u)(m−1) of the u_(th) user iscompensated with the amount ΔP_(u) estimated by TPAE, and the sum ofindividual transmission power to all indoor users is calculated. Thenthe total transmitting power P_(total)(m) at the m_(th) time instant iscompared with the maximum transmission power P_(max) of the indoor smallcell eNB. If the total transmitting power P_(total)(m) at the m_(th)time instant is less than P_(max), the power P_(u)(m) of the u_(th) useris assigned for each user at the m_(th) time instant.

If the total transmitting power P_(total)(m) at the m_(th) time instantis greater than P_(max), then the feedback loop of the step poweradjustment is performed, wherein the previous transmit power P_(u)(m−1)of the u_(t), user at the (m−1)_(th) time instant is temporarily storedin P_(tmp,u)(i) for u=1, 2 . . . nUE. In the feedback loop of the steppower adjustment, P_(tmp,u)(i) will be decreased by Δ when ΔP_(u)>0;P_(tmp,u)(i) will be increased by Δ when ΔP_(u)<0 and Δ is assumed to be±0.1 dB. After each step power adjustment loop, the sum ofP_(tmp,u)(i+1) at the (i+1)_(th) loop for all users is compared themaximum transmission power P_(max) of the indoor small cell eNB. If thetotal transmitting power P_(total)(+1) at the (i+1)_(th) loop is greaterthan P_(max), the transmit power P_(u)(m) is equal to P_(tmp,u)(i) atthe i_(th) loop and assigned to the u_(th) user at the m_(th) timeinstant. If the total transmitting power P_(total)(i+1) at the(i+1)_(th) loop is less than P_(max), i is increased by one and feedbackto the loop of the step power adjustment.

(F) Protection Mechanism of the SOPC:

The protection mechanism of the SOPC is included in the IDCC device toprevent the co-channel interference from the moving users of adjacentcells. The SODCC device inputs the average path loss measured from theUE, and then by the indoor path loss model of (16) to estimate thedistance (d) between the UE and the eNB (base station). If the moving UEis inside the coverage range of the radius (R), then the SOPC unit isinitiated to transmit the minimum power to the moving UE of the adjacentcells. Otherwise, he SOPC unit is disabled to the moving UE of theadjacent cells.

(G) Experimental Results:

FIG. 22 shows the simulation procedure to verify the servicereliability. In the experiment, the SINR are measured at uniformlydistributed UE positions in an indoor office with coverage radius R. TheSINR are measured 1000 times at each UE position. The total number ofmeasurements is defined as:

$\begin{matrix}{m_{R} = {\sum\limits_{r = 1}^{R}{7r \times 1000}}} & (27)\end{matrix}$

On the circumference of radius r=1 meter, the SINRs are measured at 7uniformly distributed positions; on the circumference of the radius r=2,3, 4, 5 meters, the corresponding uniformly distributed positions are14, 21, 28, 35, respectively. When the coverage range of indoor officeis set as 5 meters, the total number of positions to measure the SINR inan indoor office is 105. The total number of measurement positionsincreases with the coverage range of femtocell in the indoor office.

The complementary cumulative distribution function (CCDF) of themeasured SINR can be expressed as

F(SINR_(th))=P(measured SINR>SINR_(th))  (28)

The CCDF has the same meaning with the system reliability, which isdefined as the percentage of the UE locations within a eNB coverage areaof radius R for which the measured SINR exceeds a given SINR_(th). FIG.18 shows the CCDF of the measured SINR for coverage radius of 5 meter,(90%) and three different cell edge CQIs 3, 7 and 10 corresponding tothe minimum throughput requirements of three users UE1, UE2 and UE3,2.76 Mbps, 7.44 Mbps and 7.44 Mbps, respectively, in the interferencefree environments.

The SINR service reliabilities of the SOPC for coverage radius of 5meter, service reliability 90% and different cell edge CQI CQI_(min)=3,7, 10 in the interference environments are also verified with FIG.23(a), (b), (c) for interference power-100 dBm, −90 dBm and −80 dBm,respectively, where the solid curves denote for combining ITPSC and SOPCand the dot curves denote for using the ITSPC only. By means of Table 3,it can be found that the SINR_(th) values corresponding to cell edge CQI3, 7, and 10 are 5 dB, 13 dB, and 22 dB, respectively. The throughputservice reliabilities for coverage radius of 5 meter and different celledge CQI CQI_(min)=3, 7, 10 in the interference environments are alsoverified with FIG. 24(a), (b), (c) for interference power −100 dBm, −90dBm and −80 dBm, respectively, where the solid curves denote forcombining ITPSC and SOPC and the dot curves denote for using the ITSPConly. Then (7) is used to calculate the throughputs of UE1, UE2 and UE3,which are 2.76 Mbps, 7.44 Mbps and 14.13 Mbps corresponding to cell edgeCQI 3, 7, and 10, respectively. For example, as shown in FIGS. 23(a),(c) and 24(a), (c), both the CCDF of the measured SINR in 22 dB and theCCDF of the throughput in 21.4 Mbps are about 91% when the interferencepower is small (−100 dBm) for both cases of combining ITPSC and SOPCmethod and using the ITPSC only. Increasing the interference power to−80 dBm, for example, when using the ITPSC only, the CCDF of themeasured SINR value in 22 dB and the CCDF of the throughput in 14.13Mbps for UE3 is dropped to about 15%, while the SOPC is still able tomaintain about 89%. With the increase of interference power, it isobserved that using the ITPSC only is unable to maintain the servicereliability requirements set by the user, but using the SOPC is stillable to maintain the requested service reliability.

FIG. 25 and FIG. 26 show average throughput and average transmit power,respectively, for comparing the IDCC with fixed transmit power in thedifferent interference environments. FIG. 25 shows using the fixedtransmit power the average throughputs of UE1, UE2, UE3 and totalaverage throughput for −100 dBm interference power are 10.86 Mbps, 17.38Mbps, 24.08 Mbps and 52.45 Mbps, respectively. When the interferencepower increases to −80 dBm, the average throughputs of UE1, UE2 UE3 andtotal average throughput reduce to 2.97 Mbps, 6.93 Mbps, 12.41 and 22.32Mbps, respectively. Using the present IDCC, the average throughputs ofUE1, UE2, UE3 and total average throughput for −100 dBm interferencepower are 10.48 Mbps, 17.38 Mbps, 24.59 Mbps and 52.45 Mbps,respectively. When the interference power increases to −80 dBm, theaverage throughputs of UE1, UE2 UE3 and total average throughput reduceto 11.48 Mbps, 17.31 Mbps, 22.97 Mbps and 51.75 Mbps, respectively. Theaverage throughputs of UE1, UE2, UE3 and total average throughput almostremain the same values when the IDCC is used. FIG. 26 shows using thepresent IDCC, the average transmit power increases as the interferencepower increases. Using the present IDCC, the average transmit power ofUE1, UE2, UE3 and total average transmit power for −100 dBm interferencepower are −39.88 dBm, −29.85 dBm, −22.36 dBm and −21.58 dBm,respectively. When the interference power increases to −80 dBm, theaverage transmit power of UE1, UE2 UE3 and total average transmit powerincrease to −24.06 dBm, −13.56 dBm, −5.896 dBm and −5.154 dBm,respectively. The average transmit power of UE1, UE2, UE3 and totalaverage transmit power adjust with the interference environmentsadaptively to satisfy the requirements of the service reliability whenthe IDCC is used.

Thus the simulation results show that the present FFD-OFDMA based IDCCdevice for indoor small cell operated in the MU and interferenceenvironments to self-optimize the service reliability, throughput at thecell edge, minimum transmit power and interference for multimedia callservices. Thus the IDCC device can achieve the goals of saving powerconsumption and reducing co-channel interference. In this embodiment ofthe simulation, the basic OFDM transceiver parameters listed in Table 2is a single antenna mode (SISO), the present invention is alsoapplicable to multi-antenna mode (MIMO) and other different channelenvironments.

While the preferred embodiment of the invention has been set forth forthe purpose of disclosure, modifications of the disclosed embodiment ofthe invention as well as other embodiments thereof may occur to thoseskilled in the art. Accordingly, the appended claims are intended tocover all embodiments which do not depart from the spirit and scope ofthe invention.

What is claimed is:
 1. A frequency division duplexing-orthogonalfrequency division multiplexing access (FDD-OFDMA) based adaptive neuralfuzzy inference system (ANFIS) intelligent deployment cascade control(IDCC) device for indoor small cell operated in the multi-user (MU) andinterference environments to self-optimize the service reliability,throughput, minimum transmit power and interference for multimedia callservices, comprising: using the frequency division duplexing-orthogonalfrequency division multiplexing access method to allocate the resourcesof the indoor small cell base station to multiple user equipments (UEs);a resource allocator assigns the average resource blocks (RBs) of smallcell for each indoor user according to the total number of indoor usersand the setting system bandwidth; a minimum throughput/cell edge channelquality index (CQI) converter sets the cell edge (minimum) channelquality index for each indoor user in accordance with the minimumthroughput requirement; an adaptive neural fuzzy inference system basedinitial transmit power setting controller (ITPSC) in the first cascadeunit adapts the initial power setting for the uth user to the coverageradius of indoor office, the number of the resource blocks and the celledge channel quality index; an adaptive neural fuzzy inference systembased channel quality index decision controller (CQIDC) in the secondcascade unit adapts the best channel quality index to the initial powersetting, number of the resource blocks and average path loss (PL)measured by the user equipment; and an adaptive neural fuzzy inferencesystem based self-optimizing power controller (SOPC) in the thirdcascade unit consists of three parts, namely the transmit poweradjustment estimator (TPAE), transmission power assignment andself-optimization power controller protection mechanism; wherein, it canautonomously cascade control the assignments of initial power, the bestchannel quality index and the minimum transmit power to the transceiveraccording to the user input parameters including the servicereliability, coverage radius and the throughput at the cell edge; themeasured average path loss and average signal-to-interference-plus-noiseratio (SINR), so that the present intelligent deployment cascade controldevice can self-optimize the service reliability of the indoor smallcell in the multi-user and interference environments, while maintainingthe blocking error rate (BLER) less than 10-1 and minimizing thetransmit power and interference power to achieve the design aims ofenergy saving and interference reducing.
 2. The frequency divisionduplexing-orthogonal frequency division multiplexing access basedadaptive neural fuzzy inference system intelligent deployment cascadecontrol device for indoor small cell operated in the multi-user andinterference environments of claim 1, wherein the architecture ofadaptive neural fuzzy inference system based initial transmit powersetting controller unit contains five tiers, a total of three inputs andone output; three input parameters for the u_(th) user are the coverageradius of indoor office (R_(u)), the number of resource blocks (nRB_(u))and the cell edge channel quality index that is defined as CQI_(min,n),the output parameter for the u_(th) user is an initial minimum transmitpower (P_(ini,u)); each input uses three generalized bell shapemembership functions (MFs); each MF contains three levels; the 27 fuzzyinference rules are constructed; a minimum transmit power optimizationproblem of the adaptive neural fuzzy inference system—initial transmitpower setting controller is formally formulated as follows:${{{optimize}\mspace{14mu} P_{ini}} = {f\left( \overset{\rightarrow}{x} \right)}},{{{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {objective}\mspace{14mu} {function}}\;;}$subject  to:$\overset{\rightarrow}{x} \in \left\{ {R_{u},{nRB}_{u},{CQI}_{\min,u}} \right\}$0  m < R_(u) ≤ 15  m 1 ≤ nRB_(u) ≤ 100 1 ≤ CQI_(min , u) ≤ 15${\sum\limits_{u}^{nUE}P_{{ini},u}} \in {\left\{ {\leq {20\mspace{14mu} {dBm}}} \right\}.}$3. The frequency division duplexing-orthogonal frequency divisionmultiplexing access based adaptive neural fuzzy inference systemintelligent deployment cascade control device for indoor small celloperated in the multi-user and interference environments of claim 2,wherein, for the adaptive neural fuzzy inference system based initialtransmit power setting controller in the multi-user environments ofclaim 2, wherein the training data is generated from the simulationresults of the transceiver blocking error rate to train the premise andconsequent parameters of the initial transmit power setting controller;the minimum transmit power (dBm) training data of the initial transmitpower setting controller for the u_(th) user is given by:P _(ini,u) =P _(rmin,u)(CQI_(min,u))+L _(t) −G _(t)+PL(R_(u))+FM(SR_(u))−G _(r) +L _(r); wherein P_(rmin,u)(CQI_(min,u)) is thereceiver sensitivity of the cell edge channel quality index(CQI_(min,u)) for the u_(th) user; L_(t) denotes the cable loss in dB;G_(t) and G_(r) are the antenna gains in dBi of the femtocell and theuser equipment, respectively; PL(R_(u)) denotes the maximum path lossbetween a femtocell and the u_(th) user at the cell edge. L_(r) in dB isthe body loss of the user equipment; FM(SR_(u)) denotes fade margin indB corresponding to the SR set by the u_(th) user; the receiversensitivity of the given cell edge channel quality index (CQI_(min,u))for the u_(th) user is obtained byP _(rmin,u)(CQI_(min,u))=P _(N,u)+SNR_(th)(CQI_(min,u)); whereinSNR_(th)(CQI_(min,u)) denotes the SNR threshold of the receiver fordifferent CQI_(min,u), which is generated from the performancesimulations using the transceiver; the training data for minimumtransmit power is generated for the service reliability of 90%,different coverage radius (2.5, 5, 7.5, 10, 12.5 and 15 meters),different resource block (1˜100) and cell edge channel quality index(1˜15).
 4. The frequency division duplexing-orthogonal frequencydivision multiplexing access based adaptive neural fuzzy inferencesystem intelligent deployment cascade control device for indoor smallcell operated in the multi-user and interference environments of claim1, wherein the architecture of the adaptive neural fuzzy inferencesystem based channel quality index decision controller unit containsfive tiers, a total of three inputs and one output for the u_(th) user;there are three input parameters for the u_(th) user including path loss(PL _(u)) between orthogonal frequency division multiplexing (OFDM)transmitter and receiver, initial power setting (P_(ini,u)) and numberof the resource blocks; in the interference free environments, thechannel quality index decision controller unit adapts the best channelquality index (CQI_(best,u)) to the changing initial power setting,number of the resource blocks and the measured average path loss; theGaussian shape membership function of each input parameter is dividedinto three levels; there are 27 fuzzy inference rules; an optimizationproblem of channel quality index the best channel quality index of theadaptive neural fuzzy inference system based channel quality indexdecision controller is formally formulated as follows: in theinterference free environments, BLER ≤ 0.1; optimize CQI_(best,u) = f({right arrow over (x)})at the u_(th)user, f ({right arrow over (x)}) isthe objective function: subject to : {right arrow over (x)} ∈ {PL _(u),P_(ini,u),nRB_(u)} 30dB ≤ PL _(u) ≤ 70dB −75dBm ≤ P_(ini,u) ≤ 20dBm 1 ≤nRB_(u) ≤ 100 CQI_(best,u)∈ {1 ~ 15} .


5. The frequency division duplexing-orthogonal frequency divisionmultiplexing access based adaptive neural fuzzy inference systemintelligent deployment cascade control device for indoor small celloperated in the multi-user and interference environments of claim 4,wherein, for the adaptive neural fuzzy inference system based channelquality index decision controller, the training data is used to trainthe premise and consequent parameters of the channel quality indexdecision controller; the training data of the best channel quality index(CQI_(bext,u)) at the u_(th) user's location of the indoor office in theinterference free environment is determined by the following rules:${CQI}_{{best},u} = \left\{ \begin{matrix}{{CQI\_ i},} & {{{{if}\mspace{14mu} {{SNR}_{th}({CQI\_ i})}} \leq {SNR}_{u} < {{SNR}_{th}\left( {{CQI\_ i} + 1} \right)}},{i = {1\text{∼}14}}} \\{{{CQI\_}15},} & {{{if}\mspace{14mu} {SNR}_{u}} \geq {{SNR}_{th}\left( {{CQI\_}15} \right)}} \\{{{CQI\_}1},} & {{{if}\mspace{14mu} {SNR}_{u}} < {{SNR}_{th}\left( {{CQI\_}1} \right)}}\end{matrix} \right.$ the signal-to-noise-power ratio (SNR) is estimatedby:SNR_(u) =P _(r,u)(W)/P _(N,u)(W); wherein the average received powerP_(r,u) at the u_(th) user in the interference free environment is givenas:P _(r,u) =P _(ini,u) −L _(t) +G _(t)−PL _(u) +G _(r) −L _(r); wherein PL_(u) denotes the measured average path loss between a femtocell eNB andan UE in the given cell; P_(N,u) is the noise power for the u_(th) user;the training data of the channel quality index decision controller isgenerated from the simulation results of the transceiver blocking errorrate for different measured average path loss (30 dB˜70 dB), theresource block (1˜100) and initial minimum transmit power (−75 dBm˜20dBm).
 6. The frequency division duplexing-orthogonal frequency divisionmultiplexing access based adaptive neural fuzzy inference systemintelligent deployment cascade control device for indoor small celloperated in the multi-user and interference environments of claim 1,wherein the self-optimizing power controller unit consists of threeparts, namely a transmit power adjustment estimator, a transmissionpower assignment and an self-optimization power controller protectionmechanism; wherein the adaptive neural fuzzy inference system basedtransmit power adjustment estimator in the interference environmentprimarily adapts the transmit power to the requested throughput at thecell edge (corresponding to the cell edge channel quality index), thebest channel quality index and measured averagesignal-to-interference-plus-noise ratio and estimates the amount ofminimum transmit power adjustment needs for each user; wherein thetransmission power assignment adjusts the power for each indoor userwhen the sum of total transmission power to all indoor users doesn'texceed the maximum transmit power limit of the eNB; wherein a protectionmechanism for self-optimizing power controller is used to prevent theco-channel interference from the moving users of adjacent cells.
 7. Thefrequency division duplexing-orthogonal frequency division multiplexingaccess based adaptive neural fuzzy inference system intelligentdeployment cascade control device for indoor small cell operated in themulti-user and interference environments of claim 6, wherein theadaptive neural fuzzy inference system based transmit power adjustmentestimator in the self-optimizing power controller unit contains fivetiers; wherein the transmit power adjustment estimator unit acceptsthree inputs and generates the optimizing minimum transmit power;wherein three inputs for the u_(th) user including cell edge channelquality index (CQI_(min,u)), best channel quality index (CQI_(bext,u))and average measured signal-to-interference-plus-noise ratio (SINR _(u))and one power adjustment output for the u_(th) user; the generalizedbell shape membership function of each input parameter is divided intothree levels; there are 27 fuzzy inference rules; an optimizationproblem of transmit power adjustment estimator the minimum transmitpower of the adaptive neural fuzzy inference system based transmit poweradjustment estimator in the self-optimizing power controller unit isformally formulated as follows:${{in}\mspace{14mu} {the}\mspace{14mu} {interference}\mspace{14mu} {environment}},{{BLER} \leqq 0.1},{{{optimize}\mspace{14mu} \Delta \; P_{u}} = {{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {at}\mspace{14mu} {the}\mspace{14mu} u_{th}\mspace{14mu} {user}}},{{f\left( \overset{\rightarrow}{x} \right)}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {objective}\mspace{14mu} {function}\text{:}}$$\overset{\rightarrow}{x} \in \left\{ {{CQI}_{\min,u},{{CQI}_{{best},u}\mspace{14mu} {and}\mspace{14mu} {\overset{\_}{SINR}}_{u}}} \right\}$1 ≤ CQI_(min , u) ≤ 15$1 \leq {CQI}_{{best},u} \leq {15 - {25\mspace{14mu} {dB}}} \leq {\overset{\_}{SINR}}_{u} \leq {45\mspace{14mu} {dB}}$${{{\sum\limits_{u}^{nUE}P_{u}} + {\Delta \; P_{u}}} \in \left\{ {\leq {20\mspace{14mu} {dBm}}} \right\}},{P_{u}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {transmit}\mspace{14mu} {power}\mspace{14mu} ({dBm})}$at  the  last  instant.
 8. The frequency divisionduplexing-orthogonal frequency division multiplexing access basedadaptive neural fuzzy inference system intelligent deployment cascadecontrol device for indoor small cell operated in the multi-user andinterference environments of claim 7, wherein, for the transmit poweradjustment estimator, the training data is generated to train thepremise and consequent parameters of the transmit power adjustmentestimator; for the purpose of satisfying the requirements of blockingerror rate ≤10% and the SR_(u), the target threshold of the signal tointerference plus noise ratio (SINR_(th,u)) at the u_(th) user isdefined as;SINR_(th,u)=max{SNR_(th)(CQI_(min,u))+FM(SR_(u)),SNR_(th)(CQI_(best,u))}(dB);The output power adjustment (ΔP_(u)) at the u_(th) user is given bysubtracting the SINR_(th,u) from the measured averagesignal-to-interference-plus-noise ratio at the u_(th) user; using thedefined SINR_(th,u), the training data of the transmit power adjustmentvalue ΔP_(u) is generated for service reliability (90%), cell edgechannel quality index (1˜15), measured averagesignal-to-interference-plus-noise ratio (−25 dB˜45 dB) and the bestchannel quality index (1˜15).
 9. The frequency divisionduplexing-orthogonal frequency division multiplexing access basedadaptive neural fuzzy inference system intelligent deployment cascadecontrol device for indoor small cell operated in the multi-user andinterference environments of claim 7, wherein, for the protectionmechanism of the self-optimizing power controller, the intelligentdeployment cascade control device inputs the average path loss measuredfrom the user equipment, and then by the indoor path loss model toestimate the distance (d) between the user equipment and the eNB; if themoving user equipment is inside the coverage range of the radius, thenthe transmission power assignment of the self-optimizing powercontroller unit is initiated to transmit the minimum power to the movinguser equipment of the adjacent cells; otherwise, the transmission powerassignment of the self-optimizing power controller unit is disabled tothe moving user equipment of the adjacent cells.
 10. The frequencydivision duplexing-orthogonal frequency division multiplexing accessbased adaptive neural fuzzy inference system intelligent deploymentcascade control device for indoor small cell operated in the multi-userand interference environments of claim 1, wherein the device is based onthe frequency division duplexing-orthogonal frequency divisionmultiplexing access method, and uses the adaptive neural fuzzy inferencesystem architecture to adapt the initial power setting to the requestedresource block, throughput at the cell edge and coverage radius in theinterference free environment; to adapt the best channel quality indexto the initial setting power, number of the resource blocks and averagepath loss measured by user equipment in the interference freeenvironment; to adapt the transmit power assignment to the requestedthroughput at the cell edge, the best channel quality index and measuredaverage signal-to-interference-plus-noise ratio in the interferenceenvironment; the present intelligent deployment cascade control deviceis designed to self-optimize the signal-to-interference-plus-noise ratioand throughput service reliability of the indoor small cell in themulti-user and interference environments, while maintaining the blockingerror rate less than 10⁻¹ and minimizing the transmit power andinterference power to achieve the aims of energy saving and interferencereducing.